Thus far, we have been purposely nonspecific in the definition of the parametric function \(\Psi (\cdot \,;\theta )\)  in (REF ) in order to highlight the generality of the proposed unfolded algorithm.
We have only identified differentiability with respect to \(\theta \)  as a requirement to minimize (REF ) via stochastic gradient descent.
In this section, we further winnow the set of candidate functions \(\Psi \)  by requiring permutation equivariance.
As will become apparent, graph neural networks (GNNs) arise as natural candidates for the function \(\Psi \) .
GNNs are the natural extensions of convolutional neural networks to graph domains, where the architectures are appropriate to exploit non-Euclidean data.
In a nutshell, the classical convolutional layers are replaced by (banks of) graph filters [1]} that describe learnable local aggregation operations.
